| Cross validation for the ridge regression {Compositional} | R Documentation |
Cross validation for the ridge regression
Description
Cross validation for the ridge regression is performed. There is an option for the GCV criterion which is automatic.
Usage
ridge.tune(y, x, nfolds = 10, lambda = seq(0, 2, by = 0.1), folds = NULL,
ncores = 1, seed = NULL, graph = FALSE)
Arguments
y |
A numeric vector containing the values of the target variable. If the values are proportions or percentages, i.e. strictly within 0 and 1 they are mapped into R using the logit transformation. |
x |
A numeric matrix containing the variables. |
nfolds |
The number of folds in the cross validation. |
lambda |
A vector with the a grid of values of |
folds |
If you have the list with the folds supply it here. You can also leave it NULL and it will create folds. |
ncores |
The number of cores to use. If it is more than 1 parallel computing is performed. |
seed |
You can specify your own seed number here or leave it NULL. |
graph |
If graph is set to TRUE the performances for each fold as a function of the |
Details
A k-fold cross validation is performed. This function is used by alfaridge.tune.
Value
A list including:
msp |
The performance of the ridge regression for every fold. |
mspe |
The values of the mean prediction error for each value of |
lambda |
The value of |
performance |
The minimum MSPE. |
runtime |
The time required by the cross-validation procedure. |
Author(s)
Michail Tsagris.
R implementation and documentation: Giorgos Athineou <gioathineou@gmail.com> and Michail Tsagris mtsagris@uoc.gr.
References
Hoerl A.E. and R.W. Kennard (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1):55-67.
Brown P. J. (1994). Measurement, Regression and Calibration. Oxford Science Publications.
See Also
Examples
y <- as.vector(iris[, 1])
x <- as.matrix(iris[, 2:4])
ridge.tune( y, x, nfolds = 10, lambda = seq(0, 2, by = 0.1), graph = TRUE )